Volumetric Calculation of Quantization Error in 3-D Vision Systems

TL;DR

This paper presents a novel method for accurately calculating the volumetric quantization error in 3-D vision systems, improving over previous approximations by directly mapping pixel-to-scene point correspondences.

Contribution

It introduces a reverse approach to quantify uncertainty regions in 3-D mapping, avoiding complex shape approximations and providing more precise error bounds.

Findings

Earlier studies overestimated quantization error by at least a factor of two.

The method enables volumetric scene geometry determination without disparity maps.

Dependence of uncertainty volume on camera parameters is characterized.

Abstract

This paper investigates how the inherent quantization of camera sensors introduces uncertainty in the calculated position of an observed feature during 3-D mapping. It is typically assumed that pixels and scene features are points, however, a pixel is a two-dimensional area that maps onto multiple points in the scene. This uncertainty region is a bound for quantization error in the calculated point positions. Earlier studies calculated the volume of two intersecting pixel views, approximated as a cuboid, by projecting pyramids and cones from the pixels into the scene. In this paper, we reverse this approach by generating an array of scene points and calculating which scene points are detected by which pixel in each camera. This enables us to map the uncertainty regions for every pixel correspondence for a given camera system in one calculation, without approximating the complex shapes. The dependence of the volumes of the uncertainty regions on camera baseline length, focal length, pixel size, and distance to object, shows that earlier studies overestimated the quantization error by at least a factor of two. For static camera systems the method can also be used to determine volumetric scene geometry without the need to calculate disparity maps.